并行技术在注塑模CAD／CAM／CAE中的应用

传统的注塑模设计方法，是基于一种串行处理方法。需要较长的设计时间，这一过程耗时，效率低，不能适应生产迅速发展的需要。本文提出了基于并行技术的模具设计，使得ＣＡＤ／ＣＡＭ／ＣＡＥ系统内部达到以数据共享为基础的高度集成。还提出以同步工程原理管理注塑模新产品开发过程，充分体现了工程技术人员的思维广度芨其产品设计过程的反复性和协调性。     

注塑模CAE流动模拟技术概述

注塑模CAE软件根据塑料成型流变学、热传学和数值计算方法的基本理论,建立熔体在模具型腔内的流动、热传的物理数学模型,利用计算机图形学技术在计算机上形象,直观地模拟出在实际成型过程中熔体在模腔中的动态填充过程、保压过程和冷却过程,定量地给出成型过程的状态参数.     

基于CAE和DOE技术的薄壁塑件翘曲变形分析及优化

以某型号仪表显示器外壳为研究对象,采用CAE和DOE技术对其在注塑过程中出现的翘曲变形缺陷进行数值模拟试验,研究熔体温度、模具温度、保压压力和保压时间等成型工艺参数对翘曲变形的影响,并根据试验数据进行极差分析,进而对工艺参数进行优化,获得最佳工艺参数组合.该工艺参数组合可提高塑件注塑质量和生产效率,缩短试模周期,对薄壁塑件的生产实践具有参考意义.     

基于CAE分析的现代设计流程

~~基于CAE分析的现代设计流程$美国ANSYS公司成都办事处@雷先华     

基于CAE的塑料注射成型教学系统的研究与开发

塑料注射成型技术的发展与注射模具设计人员需求的增长十分迅速,模具设计人员学习必要的塑料成型基本原理和规律是掌握塑料注射模具设计方法的现实需要。本文在自主开发的注射成型CAE系统HSCAE的基础上,研究开发了一个基于Internet的塑料注射成型教学系统,通过建立知识化、数字化、可视化的教学工具来提高设计人员的学习效率、学习兴趣及对问题理解的深入程度。     

基于Web服务的注塑模CAE研究与应用

分析了Web服务的架构,介绍了注塑模CAE系统结构的特点和发展过程,根据注塑模CAE系统现阶段面对的困难,提出采用Web服务架构来解决,并阐述了采用Web服务架构的优越性。基于Web服务的注塑模CAE原型系统已经用于教学网络系统中。     

基于Internet/Intranet的注塑模CAE系统的研究与应用

通过对HSWebCAE的原型研究表明,采用集成的数据库管理的注塑成形模拟服务器端和支持三维图形交互操作的客户端相结合的CAE系统,能够更好地适应于企业动态联盟的技术需要。该文着重叙述了它基于中间件的体系结构和基于Java3D的三维模型显示及控制等关键技术。     

基于SolidWorks/MPI平台的注塑模设计

一、SolidWorks软件简介 SolidWorks是一款参数化建模设计软件,系统提供了强大的、基于特征的实体建模功能.该软件的三维零件设计工具,可以方便地通过拉伸、旋转、薄壁特征、抽壳、特征阵列以及打孔等操作来实现产品的设计;提供了众多的曲面创建命令,能够生成由复杂曲面产生的实体;提供了完善的装配特征功能,通过对设计模型的装配,可以发现零件设计尺寸是否正确、整个装配体的零件之间是否产生干涉.其中模具设计是SolidWorks自带的模块,可以使用一系列控制模具生成过程的集成工具来生成模具,它覆盖了初始分析到生成切削分割的整个范围.     

基于MSC.PATRAN的汽车制动器试验台CAE分析

CAE已经成为现代设计方法的主要手段和工具。利用有限元软件MSC.PATRAN建立了汽车制动器试验台的有限元模型,并对其进行了静力分析和模态分析,为试验台的设计和改进提供了理论依据。     

基于微机平台的注塑模型腔CAD/CAM一体化的实现

本文分析了注塑模型腔CAD／CAM实践中具体的曲线曲面造型处理方法，讨论了CAD／CAM实现中的各项关键技术，并成功地在微机系统上实现了注塑模型腔CAD／CAM的一体化。     

高分子材料PVT特性在线测试技术及其在注射成形CAE仿真中的应用

针对离线测试方法无法真实反映注射成形过程中高分子材料实际PVT参数的变化历程,利用这些参数进行CAE仿真会产生较大误差的问题,研发基于注塑机的高分子材料PVT关系在线测试技术和装置.分别采用在线测试和离线测试装置获得PVT参数进行注射成形CAE模拟,并将模拟结果与实际注射成形样品进行对比.结果表明利用在线测试PVT参数模拟得到的制品收缩率更接近实际值.以在线测试技术为基础建立的PVT关系状态方程可显著提高数值模拟精度,为高分子材料成形加工CAE仿真分析和先进制造提供重要技术支撑.     

General frameworks for optimization of plastic injection molding process parameters

Plastic injection molding is widely used for manufacturing a variety of parts. Molding conditions or process parameters play a decisive role that affects the quality and productivity of plastic products. This work reviews the state-of-the-art of the process parameter optimization for plastic injection molding. The characteristics, advantages, disadvantages, and scope of application of all of the common optimization approaches such as response surface model, Kriging model, artificial neural network, genetic algorithms, and hybrid approaches are addressed. In addition, two general frameworks for simulation-based optimization of injection molding process parameter, including direct optimization and metamodeling optimization, are proposed as recommended paradigms. Two case studies are illustrated in order to demonstrate the implementation of the suggested frameworks and to compare among these optimization methods. This work is intended as a contribution to facilitate the optimization of plastic injection molding process parameter.     

Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization简

In view of the high energy consumption and low response speed of the traditional hydraulic system for an injection molding machine, a servo motor driven constant pump hydraulic system is designed for a precision injection molding process, which uses a servo motor, a constant pump, and a pressure sensor, instead of a common motor, a constant pump, a pressure pro-portion valve, and a flow proportion valve. A model predictive control strategy based on neurodynamic optimization is proposed to control this new hydraulic system in the injection molding process. Simulation results showed that this control method has good control precision and quick response.     

注射成型弧形流道流场分析

针对在注射成型过程中,注射熔体的均匀程度以及其进入型腔的速度方向对制件的品质会产生很大影响的问题,主要对熔体进入型腔前的流动进行分析.采用弧形流道,用有限差分法离散连续性方程和动量方程,用超松驰迭代法求解离散后的代数方程组,进而求出弧形流道熔体流动的速度和压强分布.结果表明,在弧形流道中,外层熔体可能进入中心层甚至内层,局部有旋涡,内外层熔体有物质和能量交换,所以熔体经过弧形流道后密度和温度场分布更均匀.     

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.     

Expert systems in engineering design: an application for injection molding of plastic parts

The rich development environments of expert system shells provide tools for developing knowledge-based expert systems for complex tasks such as engineering design. The diverse knowledge in engineering design combines heuristics with deterministic knowledge, from multiple sources, and involves complicated, interrelated components. The hybrid capabilities in emerging expert system tools effectively address this diverse knowledge, and facilitate the rapid development of design aids by the engineering designer. A prototype expert system for injection-molded plastic parts demonstrates the utility of expert systems for design applications. The prototype is implemented in an object-oriented, rule-based environment, and incorporates solid modeling software and external material databases.     

注射合模机立柱应力分析和结构改进

针对某注射合模机立柱螺纹根部开裂问题,采用ANSYS Workbench建立单根立柱有限元模型,对某三梁四柱式注射合模机进行强度分析,得到立柱与螺母啮合处的应力大小和分布。在轴向载荷作用下,应力峰值出现在立柱与螺母啮合的第一个螺纹处,其中轴向应力分量最大;若在立柱与螺母啮合的第一个螺纹处进行圆弧过渡,可有效降低应力集中;与无圆弧半径比较,当过渡圆弧半径r=7 mm时,最大von Mises等效应力下降46%。     

Parametric design and process parameter optimization for bra cup molding via response surface methodology

Seamless and traceless undergarments have rendered foam sheet molding as an important manufacturing technique for the intimate apparel industry. Seamless bra cups are made by one-step forming technology. The three-dimensional (3D) cup shape is formed by using high temperatures and pressures with flexible polyurethane foams. Nevertheless, the mold head design process and control of the bra cup molding process are highly complicated and error prone. There is limited knowledge about the effects of foam properties, molding parameters and foam cup geometric parameters on molding process optimization. This research presents a response surface methodology as the approach for parametric design and process parameter optimization of bra cup molding. The proposed approach integrates 3D scanning via reverse engineering, parameterized-based remeshing and registration algorithm, non-linear mathematical prediction models for cup shape conformity, a model of foam shrinkage and example-based bra cup design and grading to optimize the bra cup development and production process. The experimental results show that this method is highly effective and more timesaving in the design and development of new products, as well as providing consistent quality control of the bra cup molding process.     

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

This paper presents a neural‐network‐based predictive control (NPC) method for a class of discrete‐time multi‐input multi‐output (MIMO) systems. A discrete‐time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real‐time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31 from Texas Instruments. Two examples, including the control of a MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Research on quality performance conceptual design based on SPEA2+

In order to solve the multi-objective performance optimal problems, SPEA2+ is used to realize the performance design of injection molding machine. The optimization objectives are constructed to maximize mould control power, maximize injection quantity and minimize injection power. The mathematical model is found to optimize the problem. A solution is extracted to eliminate the imprecise nature of preference through the Pareto optimal set based on fuzzy set theory. Compared with NSGA-II and SPEA2, SPEA2+ could acquire the Pareto front with better distribution and smaller distance with the optimum solutions. Finally, the case illustration of HTG1000X3Y injection molding machine is taken as an example to demonstrate that such method is effective and practical. Effective references could be provided to decision makers for objectives tradeoff at the performance conceptual design stage of injection molding machine.